{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"center\"> Visualizing Decision Trees with Python (Scikit-learn, Graphviz, Matplotlib) </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to Fit a Decision Tree Model using Scikit-Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to visualize decision trees, we need first need to fit a decision tree model using scikit-learn. If this section is not clear, I encourage you to read my [Understanding Decision Trees for Classification (Python) tutorial](https://towardsdatascience.com/understanding-decision-trees-for-classification-python-9663d683c952) as I go into a lot of detail on how decision trees work and how to use them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Load the Dataset\n",
    "The Iris dataset is one of datasets scikit-learn comes with that do not require the downloading of any file from some external website. The code below loads the iris dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = load_iris()\n",
    "df = pd.DataFrame(data.data, columns=data.feature_names)\n",
    "df['target'] = data.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting Data into Training and Test Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![images](../images/trainTestSplit.png)\n",
    "The colors in the image indicate which variable (X_train, X_test, Y_train, Y_test) the data from the dataframe df went to for a particular train test split. Image by [Michael Galarnyk](https://twitter.com/GalarnykMichael)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(df[data.feature_names], df['target'], random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scikit-learn 4-Step Modeling Pattern\n",
    "\n",
    "<b>Step 1:</b> Import the model you want to use\n",
    "\n",
    "In sklearn, all machine learning models are implemented as Python classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This was already imported earlier in the notebook so commenting out\n",
    "#from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 2:</b> Make an instance of the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier(max_depth = 2, \n",
    "                             random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 3:</b> Training the model on the data, storing the information learned from the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model is learning the relationship between x (features: sepal width, sepal height etc) and y (labels-which species of iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=2, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=0, splitter='best')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 4:</b> Predict the labels of new data (new flowers)\n",
    "\n",
    "Uses the information the model learned during the model training process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uses the information the model learned during the model training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returns a NumPy Array\n",
    "# Predict for One Observation (image)\n",
    "clf.predict(X_test.iloc[0].values.reshape(1, -1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict for Multiple Observations (images) at Once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 0, 2, 0, 2, 0, 1, 1, 1])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(X_test[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measuring Model Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>This part is not in the blog post, but I figured I would include it.</b> While there are other ways of measuring model performance (precision, recall, F1 Score, [ROC Curve](https://towardsdatascience.com/receiver-operating-characteristic-curves-demystified-in-python-bd531a4364d0), etc), we are going to keep this simple and use accuracy as our metric. \n",
    "To do this are going to see how the model performs on new data (test set)\n",
    "\n",
    "Accuracy is defined as:\n",
    "(fraction of correct predictions): correct predictions / total number of data points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8947368421052632\n"
     ]
    }
   ],
   "source": [
    "score = clf.score(X_test, Y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to Visualize Decision Trees using Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As of scikit-learn version 21.0 (roughly May 2019), Decision Trees can now be plotted with matplotlib using scikit-learn's `tree.plot_tree` without relying on the dot library which is a relatively hard-to-install dependency. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1, ncols = 1, figsize = (4,4), dpi = 300)\n",
    "\n",
    "tree.plot_tree(clf);\n",
    "fig.savefig('../images/plottreedefault.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Putting the feature names and class names into variables\n",
    "fn = ['sepal length (cm)','sepal width (cm)','petal length (cm)','petal width (cm)']\n",
    "cn = ['setosa', 'versicolor', 'virginica']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1, ncols = 1, figsize = (4,4), dpi = 300)\n",
    "\n",
    "tree.plot_tree(clf,\n",
    "               feature_names = fn, \n",
    "               class_names=cn,\n",
    "               filled = True);\n",
    "fig.savefig('../images/plottreefncn.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to Visualize Decision Trees using Graphviz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![images](../images/graphvizCTblog.png)\n",
    "The image above is a Decision Tree I produced through Graphviz. Graphviz is open source graph visualization software. Graph visualization is a way of representing structural information as diagrams of abstract graphs and networks. In data science, one use of Graphviz is to visualize decision trees. I should note that the reason why I am going over Graphviz after covering Matplotlib is that getting this to work can be difficult. The first part of this process involves creating a dot file. A dot file is a Graphviz representation of a decision tree. The problem is that using Graphviz to convert the dot file into an image file (png, jpg, etc) can be difficult. There are a couple ways to do this including: installing python-graphviz though Anaconda, installing Graphviz through Homebrew (Mac only), installing Graphviz through executables (Windows only), and using an online converter on the content of your dot file to convert it into an image.\n",
    "![images](../images/dot2imagefile.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export your model to a dot file\n",
    "The code below code will work on any operating system as python generates the dot file and exports it as a file named tree.dot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree.export_graphviz(clf,\n",
    "                     out_file=\"tree.dot\",\n",
    "                     feature_names = fn, \n",
    "                     class_names=cn,\n",
    "                     filled = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\ntree.export_graphviz(clf,\\n                     out_file=\"treeRotated.dot\",\\n                     feature_names = fn, \\n                     class_names=cn,\\n                     rotate = True,\\n                     filled = True)\\n'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ignore this cell as I am just rotating the decision tree output. \n",
    "\"\"\"\n",
    "tree.export_graphviz(clf,\n",
    "                     out_file=\"treeRotated.dot\",\n",
    "                     feature_names = fn, \n",
    "                     class_names=cn,\n",
    "                     rotate = True,\n",
    "                     filled = True)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installing and Using Graphviz\n",
    "Converting the dot file into an image file (png, jpg, etc) typically requires installation of Graphviz which depends on your operating system and a host of other things. I highly recommend that if you get an error in the code below to see the blog and see how to install it on your operating system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "!dot -Tpng -Gdpi=300 tree.dot -o tree.png"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!dot -Tpng -Gdpi=300 treeRotated.dot -o treeRotated.png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to Visualize Individual Decision Trees from Bagged Trees or Random Forests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![images](../images/BaggedTrees.png)\n",
    "\n",
    "A weakness of decision trees is that they don't tend to have the best predictive accuracy. This is partially because of high variance, meaning that different splits in the training data can lead to very different trees.\n",
    "\n",
    "The image above could be a diagram for Bagged Trees or Random Forests models which are ensemble methods. This means using multiple learning algorithms to obtain a better predictive performance than could be obtained from any of the constituent learning algorithms alone (many trees protect each other from their individual errors). How exactly Bagged Trees and Random Forests models work is a subject for another blog, but what is important to note is that for each both models we grow N trees where N is the number of decision trees a user specifies. Consequently after you fit a model, it would be nice to look at the individual decision trees that make up your model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Dataset\n",
    "The Breast Cancer Wisconsin (Diagnostic) Dataset is one of datasets scikit-learn comes with that do not require the downloading of any file from some external website. The code below loads the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
       "0                 0.07871  ...          17.33           184.60      2019.0   \n",
       "1                 0.05667  ...          23.41           158.80      1956.0   \n",
       "2                 0.05999  ...          25.53           152.50      1709.0   \n",
       "3                 0.09744  ...          26.50            98.87       567.7   \n",
       "4                 0.05883  ...          16.67           152.20      1575.0   \n",
       "\n",
       "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   worst symmetry  worst fractal dimension  target  \n",
       "0          0.4601                  0.11890       0  \n",
       "1          0.2750                  0.08902       0  \n",
       "2          0.3613                  0.08758       0  \n",
       "3          0.6638                  0.17300       0  \n",
       "4          0.2364                  0.07678       0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = load_breast_cancer()\n",
    "df = pd.DataFrame(data.data, columns=data.feature_names)\n",
    "df['target'] = data.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arrange Data into Features Matrix and Target Vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.loc[:, df.columns != 'target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.loc[:, 'target'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data into training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, y, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forests in `scikit-learn` (with N = 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 1:</b> Import the model you want to use\n",
    "\n",
    "In sklearn, all machine learning models are implemented as Python classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This was already imported earlier in the notebook so commenting out\n",
    "# from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 2:</b> Make an instance of the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'RandomForestClassifier' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-469d64726213>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m rf = RandomForestClassifier(n_estimators=100,\n\u001b[0m\u001b[1;32m      2\u001b[0m                             random_state=0)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'RandomForestClassifier' is not defined"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(n_estimators=100,\n",
    "                            random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 3:</b> Training the model on the data, storing the information learned from the data. Model is learning the relationship between features and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Step 4:</b> Predict the labels of new data\n",
    "\n",
    "Uses the information the model learned during the model training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Not doing this step in the tutorial\n",
    "# class predictions (not predicted probabilities)\n",
    "# predictions = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measuring Model Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>This part is not in the blog post, but I figured I would include it.</b> While there are other ways of measuring model performance (precision, recall, F1 Score, [ROC Curve](https://towardsdatascience.com/receiver-operating-characteristic-curves-demystified-in-python-bd531a4364d0), etc), we are going to keep this simple and use accuracy as our metric. \n",
    "To do this are going to see how the model performs on new data (test set)\n",
    "\n",
    "Accuracy is defined as:\n",
    "(fraction of correct predictions): correct predictions / total number of data points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = rf.score(X_test, Y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    357\n",
       "0    212\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 357 benign, 212 malignant\n",
    "df['target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['malignant', 'benign'], dtype='<U9')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data.data, columns=data.feature_names)\n",
    "df['target'] = data.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/wisconsinBreastCancer.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    357\n",
       "1    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['diagnosis'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = load_breast_cancer(return_X_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    357\n",
       "0    212\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(y)[0].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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